摘要
The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. However, there are also other problems whose modeling entails obtaining a weakly connected directed graph such as the student access to the university, the public transport networks, or trophic chains. Those cases deserve particularized study with an analysis and the resolution adjusted to them. Additionally, this is a challenge, since the existing algorithms in most of the cases were originally designed for non-directed graphs or symmetrical and regular graphs. Our proposal is a Benchmark Generator of Weakly Connected Directed Graphs whose properties can be defined by the end-users according to their necessities. The source code of the generators described in this article is available in GitHub under the GNU license.
The previous studies on detection of communities on complex networks were focused on nondirected graphs, such as Neural Networks, social networks, social interrelations, the contagion of diseases, and bibliographies. However, there are also other problems whose modeling entails obtaining a weakly connected directed graph such as the student access to the university, the public transport networks, or trophic chains. Those cases deserve particularized study with an analysis and the resolution adjusted to them. Additionally, this is a challenge, since the existing algorithms in most of the cases were originally designed for non-directed graphs or symmetrical and regular graphs. Our proposal is a Benchmark Generator of Weakly Connected Directed Graphs whose properties can be defined by the end-users according to their necessities. The source code of the generators described in this article is available in GitHub under the GNU license.
基金
supported by the Project“Complex Networks”from the Instituto Universitario de Matematica Multidisciplinar(IUMM)of the Universitat Politecnica de Valencia(UPV)[under Grant number(266500194)20170251-Complex-Networks-UPV]
